Most cold email campaigns reply below the average
Across 81 campaigns with at least 500 contacted leads, the pooled reply rate is 2.55% and the median campaign rate is 2.12%. 50 of 81 campaigns fell below the pooled rate. Fourteen campaigns contributed at least half of the subset’s unique replies. These are different ways of summarising one operator’s observations; none predicts your campaign outcome.
The distribution in numbers
Selected campaign bars, with full-subset reference lines
Each displayed bar is an eligible campaign with a per-contact reply rate of at least 1%. Lower-rate campaign bars are omitted from this selected view. The pooled and median reference lines, summary table and threshold counts still use all 81 eligible campaigns.
Quartiles describe positions, not quarter-level pooled results. The 25th percentile is 1.38%, the median 2.12% and the 75th percentile 2.97%. A percentile boundary is different from pooling the replies and contacts inside a quarter. These figures describe this sample only.
Test a threshold against the real set
Choose a hypothetical per-contact reply threshold and compare it with the 81 eligible campaigns. This retrospective count is not the probability that a new campaign will succeed and does not validate a vendor’s promise.
How many campaigns cleared it
With JavaScript unavailable, the figures shown are for a 2.50 percent threshold.
Read this the right way round. A campaign clearing a threshold does not mean the threshold is achievable on demand - these are outcomes measured after the fact, on lists and copy that varied enormously. It tells you how often it happened, not how often it can be promised.
Why weighting changes the answer
The pooled rate divides total unique replies by total campaign contacts. A campaign with 8,000 contacts contributes eight times the denominator weight of one with 1,000 contacts. The median instead sorts the 81 campaign rates and selects the middle observation.
In this subset, the pooled rate is 2.55%, the median is 2.12%, and 50 campaigns fall below the pooled rate. That is an observed shape, not a mathematical rule: another sample can have its median above its pooled rate. Fourteen campaigns contributing half the replies also does not isolate rate performance, because larger campaigns can contribute more replies through volume.
Compare a relevant cohort before using either figure
Ask for the same denominator, campaign-size rule, reply classification, time cutoff and target market. Different weighting, customer mix or automatic-reply handling can make published figures incomparable. This analysis does not isolate which factor caused a difference between vendors.
See reply-rate definitions and the canonical benchmark methodology before comparing rates.
What to ask when buying outbound
Request the denominator and the cohort
Ask whether the numerator is unique repliers, total messages or positive responses, whether automatic replies are included, and whether the denominator is contacted leads, sent messages or delivered messages. Request campaign counts and eligibility rules alongside the rate.
Request both the pooled rate and campaign percentiles
The pooled rate describes the combined book; percentiles describe how campaign-level rates vary. Neither is your expected result without a relevant comparison cohort. The 1.38% / 2.12% / 2.97% selected quartiles here are descriptive reference points, not a promised operating range.
Test your economics with explicit assumptions
Use a range of reply, held-meeting and eventual-win assumptions, including a downside case. A reply-rate study cannot supply unmeasured attendance or close rates. The cost per opportunity tool and year-one cost comparison help make those assumptions visible. Check each model’s scope before using its output.
Ask for stage evidence
Request anonymised records separating a booking, an attended meeting, a sales-accepted opportunity and a won deal. Agree rejection, replacement and attribution rules before treating replies as pipeline.
Method and limitations
Sample and eligibility
The frozen 12 August 2026 extraction contains 132 campaign rows across seven programmes: five client programmes and two internal programmes. Of these, 115 have sends and 17 are zero-send rows. The 81 campaigns with at least 500 contacted leads form this page’s analytic subset, with 236,927 summed campaign contacts and 6,038 unique replies. The 500-contact floor is an editorial inclusion rule; it does not establish statistical confidence or eliminate sampling variability.
Definitions and weighting
Per-contact reply rate is unique replies divided by contacted leads within each campaign. Automatic replies are included in all 115 sending campaigns. A reply is not necessarily positive, human, a booking or an attended meeting. Contacts are deduplicated within the platform’s campaign counter; summed counts are not globally deduplicated people across campaigns. Pooled rate is the ratio of summed counts, not the arithmetic mean of campaign percentages. Percentiles use linear interpolation between ordered campaign rates.
Snapshot timing and publication scope
These are cumulative counters at extraction, not a clean five-month activity cohort. Campaign creation dates span 1 April to 10 August 2026 and do not identify first-send dates. The summary and threshold counts use all 81 eligible campaigns; the chart explicitly selects only bars at or above a 1% per-contact rate. Individual lower-rate observations and a full-range chart are not published here. No client or recipient identifiers are disclosed.
What this cannot establish
The snapshot contains no reconciled held-meeting, accepted-opportunity or won-deal records. It cannot prove return on spend, an industry-wide success rate, or what caused a campaign’s result. Offer, targeting, copy and timing vary together. Automatic-reply handling and one operator’s selection limit generalisation.
ReplyLead publishes research about its own operation and sells outbound services. Read our methodology, editorial standards and publisher details. This page’s source is the same frozen extraction used by cold email benchmarks.
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